Abstract: Objectives: Dense and continuous optical flow plays an important role in many applications, including robot navigation, autonomous driving, motion planning, visual odometry, etc. Current related works mainly utilize shutter cameras and event cameras to output optical flow. However, dense and continuous flow estimation is still a challenge due to the fixed frame rate of shutter camera and the sparse event data. In addition, existing related approaches focus on the way how to integrate images and event data, but neglect to deal with the long-time-interval optical flow estimation. Methods: To this end, we propose a multi-scale recurrent optical flow estimation framework fusing events and images. The network architecture contains three components: multi-scale feature extractor, image-event feature fusion module and flow recurrent updater. The multi-scale feature extractor is a CNN-based downsampler capable of mapping input image and event data into features at different scales. The image-event feature fusion module is applied to fuse features from two different modalities of data. The flow recurrent updater is a recurrent residual flow optimizer, incorporated the pyramid methods, estimating flow in coarse-to-fine way as well as performing flow feature refinement. Furthermore, to avoid expensive flow annotations and perform effective network training, we train network in the unsupervised way and design a novel training strategy, namely dynamic loss filtering mechanism, to filter out redundant and unreliable supervisory signals. Results: We conduct a series of experiments on the MVSEC dataset. The results show the proposed method performs well in both indoor and outdoor sequences. In particular, for long-time-interval dense optical flow estimation, the proposed method which tested on three indoor sequences achieves optimal performance in mean endpoint error and the percentage of anomalies, which are 1.43, 1.87, and 1.68, as well as 7.54, 14.36, and 11.46%, respectively. Conclusions: The proposed method not only can perform dense and continuous optical flow estimation, but also has a remarkable advantage on long-time-interval optical flow estimation.